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Effects of the Spatial Resolution of UAV Images on the Prediction and Transferability of Nitrogen Content Model for Winter Wheat

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Deep Transfer Learning

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Site-specific nitrogen management in winter wheat supported by low-altitude remote sensing and soil data

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An assessment of multi-view spectral information from UAV-based color-infrared images for improved estimation of nitrogen nutrition status in winter wheat

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LIDAR-Based Phenotyping for Drought Response and Drought Tolerance in Potato

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A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency

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Optimization of soil-adjusted vegetation indices

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Diagnosis of winter-wheat water stress based on UAV-borne multispectral image texture and vegetation indices

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Estimation of nitrogen content in cucumber plant (Cucumis sativus L.) leaves using hyperspectral imaging data with neural network and partial least squares regressions

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Estimation of leaf nitrogen concentration in wheat using the MK-SVR algorithm and satellite remote sensing data

inferredcites →https://doi.org/10.1016/j.compag.2018.05.012openalex-snapshot

Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: A review

inferredcites →https://doi.org/10.1016/j.compag.2019.04.005openalex-snapshot

Multi-LUTs method for canopy nitrogen density estimation in winter wheat by field and UAV hyperspectral

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Rice nitrogen nutrition estimation with RGB images and machine learning methods

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Estimation of nitrogen nutrition index in rice from UAV RGB images coupled with machine learning algorithms

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Estimating pasture quality of Mediterranean grasslands using hyperspectral narrow bands from field spectroscopy by Random Forest and PLS regressions

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Retrieval model for total nitrogen concentration based on UAV hyper spectral remote sensing data and machine learning algorithms – A case study in the Miyun Reservoir, China

inferredcites →https://doi.org/10.1016/j.eja.2015.11.026openalex-snapshot

Are vegetation indices derived from consumer-grade cameras mounted on UAVs sufficiently reliable for assessing experimental plots?

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Combining fixed-wing UAV multispectral imagery and machine learning to diagnose winter wheat nitrogen status at the farm scale

inferredcites →https://doi.org/10.1016/j.fcr.2010.01.010openalex-snapshot

Measuring and predicting canopy nitrogen nutrition in wheat using a spectral index—The canopy chlorophyll content index (CCCI)

inferredcites →https://doi.org/10.1016/j.isprsjprs.2019.02.022openalex-snapshot

Estimate of winter-wheat above-ground biomass based on UAV ultrahigh-ground-resolution image textures and vegetation indices

inferredcites →https://doi.org/10.1016/j.jag.2020.102174openalex-snapshot

Retrieval of aboveground crop nitrogen content with a hybrid machine learning method

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Evaluation of random forest and regression tree methods for estimation of mass first flush ratio in urban catchments

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Estimation of soybean grain yield from multispectral high-resolution UAV data with machine learning models in West Africa

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Use of a green channel in remote sensing of global vegetation from EOS-MODIS

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Remote estimation of canopy chlorophyll content in crops

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Quantitative relationships between field-measured leaf area index and vegetation index derived from VEGETATION images for paddy rice fields

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Combining spectral and texture features of UAV hyperspectral images for leaf nitrogen content monitoring in winter wheat

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A reinforcement learning approach to rare trajectory sampling

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Optimizing Rice Near-Infrared Models Using Fractional Order Savitzky–Golay Derivation (FOSGD) Combined with Competitive Adaptive Reweighted Sampling (CARS)

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Estimation of area- and mass-based leaf nitrogen contents of wheat and rice crops from water-removed spectra using continuous wavelet analysis

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PRECISION WEED CONTROL SYSTEM FOR COTTON

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The Technology for Detecting Weeds in Agricultural Crops Based on Vegetation Index VARI (PlanetScope)

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Ground Hyper-Spectral Remote-Sensing Monitoring of Wheat Water Stress during Different Growing Stages

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Illumination Geometry and Flying Height Influence Surface Reflectance and NDVI Derived from Multispectral UAS Imagery

inferredcites →https://doi.org/10.3390/drones4030036openalex-snapshot

Measures of Canopy Structure from Low-Cost UAS for Monitoring Crop Nutrient Status

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Monitoring Onion Crop “Cipolla Rossa di Tropea Calabria IGP” Growth and Yield Response to Varying Nitrogen Fertilizer Application Rates Using UAV Imagery

inferredcites →https://doi.org/10.3390/rs11242925openalex-snapshot

Predicting Canopy Nitrogen Content in Citrus-Trees Using Random Forest Algorithm Associated to Spectral Vegetation Indices from UAV-Imagery

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Enhancing the Nitrogen Signals of Rice Canopies across Critical Growth Stages through the Integration of Textural and Spectral Information from Unmanned Aerial Vehicle (UAV) Multispectral Imagery

inferredcites →https://doi.org/10.3390/rs12203396openalex-snapshot

Estimation of Nitrogen in Rice Crops from UAV-Captured Images

inferredcites →https://doi.org/10.3390/rs12223778openalex-snapshot

Winter Wheat Nitrogen Status Estimation Using UAV-Based RGB Imagery and Gaussian Processes Regression

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Estimating the Leaf Nitrogen Content with a New Feature Extracted from the Ultra-High Spectral and Spatial Resolution Images in Wheat

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Combining Spectral and Texture Features of UAS-Based Multispectral Images for Maize Leaf Area Index Estimation

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Combining Spectral and Textural Information from UAV RGB Images for Leaf Area Index Monitoring in Kiwifruit Orchard

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Vicarious Radiometric Calibration of a Multispectral Camera on Board an Unmanned Aerial System

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Combined Spectral and Spatial Modeling of Corn Yield Based on Aerial Images and Crop Surface Models Acquired with an Unmanned Aircraft System

inferredcites →https://doi.org/10.3390/rs8090706openalex-snapshot

Monitoring Agronomic Parameters of Winter Wheat Crops with Low-Cost UAV Imagery

inferredcites →https://doi.org/10.34133/2021/9892570openalex-snapshot

Qualification of Soybean Responses to Flooding Stress Using UAV-Based Imagery and Deep Learning

inferredcites →https://doi.org/10.4236/ars.2018.72006openalex-snapshot

Assessment of UAV Based Vegetation Indices for Nitrogen Concentration Estimation in Spring Wheat

inferredcites →https://doi.org/10.5194/egusphere-egu21-11716openalex-snapshot

Winter wheat and maize under varying soil moisture: from leaf to canopy

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.4792012openalex-extracted

Optimizing Nitrogen Estimates in Common Bean Canopies Throughout Key Growth Stages Via Fusion of Spectral and Textural Data from Unmanned Aerial Vehicle Multispectral Imagery

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Estimation of leaf nitrogen concentration at different leaf positions in rice based on UAV RGB images combined with machine-learning algorithms

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Optimizing nitrogen estimates in common bean canopies throughout key growth stages via spectral and textural data from unmanned aerial vehicle multispectral imagery

inferred← citeshttps://doi.org/10.1016/j.nxsust.2026.100307openalex-snapshot

Integrating sentinel-2 vegetation indices and random forest models for nitrogen mapping in tropical agroecosystems

inferred← citeshttps://doi.org/10.1080/01431161.2026.2695948openalex-snapshot

Estimating rice chlorophyll status from UAV RGB imagery: effects of image resolution and texture feature parameterization

inferred← citeshttps://doi.org/10.1117/12.3029189openalex-snapshot

Predictive modeling of nitrogen content in winter wheat plants based on LASSO feature screening and UAV imagery

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Foliar Nitrogen Estimation with Artificial Intelligence and Technological Tools: State of the Art and Future Challenges

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Improvement of Winter Wheat Aboveground Biomass Estimation Using Digital Surface Model Information Extracted from Unmanned-Aerial-Vehicle-Based Multispectral Images

inferred← citeshttps://doi.org/10.3390/agriculture14101775openalex-snapshot

Effects of Variety and Growth Stage on UAV Multispectral Estimation of Plant Nitrogen Content of Winter Wheat

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Enhancing Turbidity Predictions in Coastal Environments by Removing Obstructions from Unmanned Aerial Vehicle Multispectral Imagery Using Inpainting Techniques

inferred← citeshttps://doi.org/10.3390/s23198089openalex-snapshot

Construction of a Winter Wheat Comprehensive Growth Monitoring Index Based on a Fuzzy Degree Comprehensive Evaluation Model of Multispectral UAV Data

inferred← citeshttps://doi.org/10.5772/intechopen.1000551openalex-snapshot

Drones - Various Applications

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Comparison and Transferability of Nitrogen Content Prediction Model Based in Winter Wheat from UAV Multispectral Image Data

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